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+# LLM 上下文窗口管理实现文档
+
+## 概述
+
+本文档说明了 GNS3 Copilot 如何处理不同 LLM 模型的上下文窗口限制，以及如何实现自动消息裁剪功能。
+
+## ⚠️ 重要：context_limit 必须手动配置
+
+**由于模型供应商频繁更新上下文窗口大小，系统不再提供内置默认值。**
+
+用户在创建 LLM 模型配置时**必须提供 `context_limit`**。请从模型供应商的官方文档获取最新的上下文窗口大小。
+
+### ⚡ 单位说明
+
+**`context_limit` 的单位是 K tokens（千 tokens）**
+
+- `1` = 1K = 1,000 tokens
+- `128` = 128K = 128,000 tokens
+- `200` = 200K = 200,000 tokens
+- `2800` = 2800K = 2,800,000 tokens
+
+### 为什么使用 K tokens？
+
+1. **更简洁** - `128` 比 `128000` 更易读易写
+2. **减少错误** - 避免少写或多写 0
+3. **符合习惯** - 与业界常用的 "128K", "200K" 表示法一致
+
+### 为什么必须手动配置？
+
+1. **模型更新频繁** - OpenAI、Anthropic、Google 等供应商经常发布模型更新
+2. **上下文大小变化** - 新版本往往增加上下文窗口，旧版本可能被废弃
+3. **维护成本高** - 内置默认值很快过时，可能导致配置错误
+4. **责任明确** - 用户根据实际使用的模型配置，避免混淆
+
+---
+
+## 数据库配置方式
+
+### 配置要求
+
+`context_limit` 是**必填字段**，单位为 **K tokens**。创建或更新 LLM 模型配置时必须提供。
+
+| 字段 | 类型 | 必填 | 单位 | 说明 |
+|------|------|------|------|------|
+| `context_limit` | `int` | ✅ **是** | K tokens | 模型上下文窗口限制（128 = 128K tokens） |
+| `context_strategy` | `"conservative" \| "balanced" \| "aggressive"` | 否 | - | 裁剪策略，默认 "balanced" |
+
+### 获取上下文窗口大小
+
+#### 官方文档链接
+
+| 供应商 | 文档链接 | 示例值（tokens） | 配置值（K） |
+|--------|---------|------------------|------------|
+| OpenAI | https://platform.openai.com/docs/models | 128,000 | `128` |
+| Anthropic | https://docs.anthropic.com/claude/docs/models-overview | 200,000 | `200` |
+| Google | https://ai.google.dev/gemini-api/docs/models | 2,800,000 | `2800` |
+| DeepSeek | https://platform.deepseek.com/api-docs/ | 128,000 | `128` |
+| xAI | https://docs.x.ai/ | 128,000 | `128` |
+
+**转换示例**：
+- 官方文档显示 `128,000 tokens` → 配置为 `128`
+- 官方文档显示 `200,000 tokens` → 配置为 `200`
+- 官方文档显示 `2,800,000 tokens` → 配置为 `2800`
+
+#### 参考工具
+
+运行参考脚本查看常见模型的上下文限制（显示为 K tokens）：
+
+```bash
+python scripts/show_model_context_limits.py
+```
+
+**注意**：此脚本仅提供参考值，请以官方文档为准。
+
+---
+
+## API 使用示例
+
+### 创建配置（必须提供 context_limit）
+
+```bash
+POST /v3/users/{user_id}/llm-model-configs
+Content-Type: application/json
+
+{
+  "name": "GPT-4o Configuration",
+  "model_type": "text",
+  "is_default": true,
+  "provider": "openai",
+  "base_url": "https://api.openai.com/v1",
+  "model": "gpt-4o",
+  "temperature": 0.7,
+  "api_key": "sk-...",
+  "context_limit": 128,         # ← 必填：单位是 K tokens (128 = 128K = 128,000 tokens)
+  "context_strategy": "balanced"  # 可选，默认 "balanced"
+}
+```
+
+### 配置示例对比
+
+#### 官方文档 → API 配置
+
+| 模型 | 官方文档 | API 配置 |
+|------|---------|---------|
+| GPT-4o | 128,000 tokens | `"context_limit": 128` |
+| GPT-3.5 Turbo | 16,385 tokens | `"context_limit": 17` (向上取整) |
+| Claude 3.5 Sonnet | 200,000 tokens | `"context_limit": 200` |
+| Gemini 1.5 Pro | 2,800,000 tokens | `"context_limit": 2800` |
+
+### 错误示例（缺少 context_limit）
+
+```bash
+POST /v3/users/{user_id}/llm-model-configs
+Content-Type: application/json
+
+{
+  "name": "Invalid Config",
+  "provider": "openai",
+  "model": "gpt-4o",
+  # ❌ 缺少 context_limit
+}
+```
+
+**响应**：
+```json
+{
+  "detail": "context_limit is required (unit: K tokens, e.g., 128 = 128K = 128,000 tokens). Please check your model provider's documentation for the current context window size and specify it in the configuration."
+}
+```
+
+---
+
+## 常见模型配置参考
+
+以下是一些常见模型的 `context_limit` 配置值（**请以官方文档为准**）：
+
+### OpenAI
+| 模型 | 官方文档 | 配置值 |
+|------|---------|-------|
+| GPT-4o | 128,000 tokens | `"context_limit": 128` |
+| GPT-4o-mini | 128,000 tokens | `"context_limit": 128` |
+| GPT-4 Turbo | 128,000 tokens | `"context_limit": 128` |
+| GPT-3.5 Turbo | 16,385 tokens | `"context_limit": 17` |
+
+### Anthropic
+| 模型 | 官方文档 | 配置值 |
+|------|---------|-------|
+| Claude 3.5 Sonnet | 200,000 tokens | `"context_limit": 200` |
+| Claude 3 Opus | 200,000 tokens | `"context_limit": 200` |
+| Claude 3 Haiku | 200,000 tokens | `"context_limit": 200` |
+
+### Google
+| 模型 | 官方文档 | 配置值 |
+|------|---------|-------|
+| Gemini 2.0 Flash | 1,000,000 tokens | `"context_limit": 1000` |
+| Gemini 1.5 Pro | 2,800,000 tokens | `"context_limit": 2800` |
+| Gemini 1.5 Flash | 2,800,000 tokens | `"context_limit": 2800` |
+
+### DeepSeek
+| 模型 | 官方文档 | 配置值 |
+|------|---------|-------|
+| DeepSeek Chat | 128,000 tokens | `"context_limit": 128` |
+| DeepSeek Coder | 128,000 tokens | `"context_limit": 128` |
+
+### xAI
+| 模型 | 官方文档 | 配置值 |
+|------|---------|-------|
+| Grok Beta | 128,000 tokens | `"context_limit": 128` |
+
+---
+
+## 问题背景
+
+### 原始问题
+- 消息历史无限制累积，使用 `operator.add` 追加所有消息
+- 不同模型的上下文窗口限制不同
+- 当消息历史超过模型限制时，LLM 调用会失败
+- 没有优雅的降级或裁剪机制
+
+### 解决方案
+使用 LangChain/LangGraph 的内置功能实现智能上下文管理：
+- **`trim_messages`**: LangChain 的消息裁剪工具
+- **`count_tokens_approximately`**: Token 计数功能
+- **用户配置**: 必须提供 context_limit（单位：K tokens），无默认值
+
+## 实现架构
+
+### 1. 核心模块
+
+**文件位置**: `gns3server/agent/gns3_copilot/agent/context_manager.py`
+
+#### 关键函数
+
+**`get_model_context_limit(model_name: str, llm_config: dict) -> int`**
+- 从数据库配置获取模型的上下文窗口大小
+- **`llm_config` 必须包含 `context_limit` 字段（单位：K tokens）**
+- 如果未提供或无效，抛出 `ValueError`
+- 返回值单位为实际 tokens（K tokens × 1000）
+
+**`calculate_max_tokens(model_limit, strategy) -> int`**
+- 计算可用 token 数量（预留输出空间）
+- 三种策略：
+  - `conservative`: 使用 60% 限制（更安全）
+  - `balanced`: 使用 75% 限制（默认）
+  - `aggressive`: 使用 85% 限制（最大化输入）
+
+**`trim_messages_for_context(messages, model_name, strategy) -> list`**
+- 使用 LangChain 的 `trim_messages` 裁剪消息
+- 保留最近的消息
+- 始终保留系统消息
+
+**`prepare_context_messages(state_messages, system_prompt, topology_context, model_name, trim_strategy) -> list`**
+- GNS3 Copilot 的主要入口点
+- 构建完整上下文（系统提示 + 拓扑信息 + 消息历史）
+- 自动裁剪以适应模型限制
+
+### 2. 集成到 GNS3 Copilot
+
+**修改文件**: `gns3server/agent/gns3_copilot/agent/gns3_copilot.py`
+
+#### 改动内容
+
+**导入新模块**:
+```python
+from gns3server.agent.gns3_copilot.agent.context_manager import (
+    prepare_context_messages,
+)
+```
+
+**替换消息构建逻辑**:
+```python
+# 旧代码（手动构建）
+full_messages = (
+    [SystemMessage(content=current_prompt)]
+    + context_messages
+    + state["messages"]
+)
+
+# 新代码（自动裁剪）
+full_messages = prepare_context_messages(
+    state_messages=state["messages"],
+    system_prompt=current_prompt,
+    topology_context=topology_context,
+    model_name=llm_config.get("model", "default"),
+    llm_config=llm_config,  # ← 包含 context_limit 和 context_strategy
+)
+```
+
+### 3. LangChain/LangGraph 的内置功能
+
+#### `trim_messages` 参数说明
+
+```python
+from langchain_core.messages.utils import trim_messages
+
+trimmed = trim_messages(
+    messages,
+    strategy="last",                    # 保留最近的 N 条消息
+    max_tokens=10000,                   # 最大 token 数
+    token_counter=count_tokens_approximately,  # Token 计数器
+    preserve_system=True,               # 保留系统消息
+    start_on="human",                   # 确保从人类消息开始
+    end_on=("human", "tool", "ai"),    # 在特定类型消息结束
+)
+```
+
+#### `count_tokens_approximately`
+
+- 快速估算消息的 token 数量
+- 不需要调用 LLM API
+- 用于判断是否需要裁剪
+
+---
+
+## 策略详细说明
+
+### Conservative（保守策略）
+
+**参数配置**：
+```json
+{
+  "context_limit": 128,
+  "context_strategy": "conservative"
+}
+```
+
+**实际效果**：
+- **使用比例**：60% (context_limit × 0.6)
+- **计算公式**：`max_tokens = context_limit × 1000 × 0.6`
+- **示例**：对于 128K 上下文限制
+  - 保留空间：`128 × 1000 × 0.6 = 76,800` tokens 用于输入
+  - 预留空间：`128,000 - 76,800 = 51,200` tokens 用于输出
+
+**适用场景**：
+1. **长输出任务**
+   - 代码生成（可能生成数百行代码）
+   - 文章写作（需要完整文章内容）
+   - 详细报告生成（包含多个章节）
+
+2. **复杂任务**
+   - 多步骤推理任务
+   - 需要深度分析的请求
+   - 综合性问题的解决方案
+
+3. **不确定输出大小时**
+   - 不确定 LLM 会返回多长的内容
+   - 首次尝试某种类型的任务
+   - 需要额外安全边界的场景
+
+**优缺点**：
+- ✅ **优点**：输出更不容易被截断，安全性高
+- ❌ **缺点**：输入上下文较少，可能遗漏早期信息
+
+**日志示例**：
+```
+INFO: Context prepared: 20 msgs, ~72800 tokens / 128K limit (56.9%), strategy=conservative
+INFO: Available for output: ~51200 tokens
+```
+
+---
+
+### Balanced（平衡策略，推荐）
+
+**参数配置**：
+```json
+{
+  "context_limit": 128,
+  "context_strategy": "balanced"
+}
+```
+
+**实际效果**：
+- **使用比例**：75% (context_limit × 0.75)
+- **计算公式**：`max_tokens = context_limit × 1000 × 0.75`
+- **示例**：对于 128K 上下文限制
+  - 保留空间：`128 × 1000 × 0.75 = 96,000` tokens 用于输入
+  - 预留空间：`128,000 - 96,000 = 32,000` tokens 用于输出
+
+**适用场景**：
+1. **一般对话**
+   - 日常聊天交互
+   - 问答式对话
+   - 技术支持和咨询
+
+2. **大多数场景**
+   - 网络配置命令生成
+   - 故障排查建议
+   - 知识问答
+
+3. **平衡输入和输出**
+   - 需要较多上下文，但输出也较长的场景
+   - 中等复杂度的任务
+
+**优缺点**：
+- ✅ **优点**：在输入上下文和输出空间之间取得良好平衡
+- ✅ **优点**：适用于大多数使用场景
+- ⚠️ **注意**：对于特别长的输出可能被截断
+
+**日志示例**：
+```
+INFO: Context prepared: 30 msgs, ~85600 tokens / 128K limit (66.9%), strategy=balanced
+INFO: Available for output: ~32000 tokens
+```
+
+---
+
+### Aggressive（激进策略）
+
+**参数配置**：
+```json
+{
+  "context_limit": 128,
+  "context_strategy": "aggressive"
+}
+```
+
+**实际效果**：
+- **使用比例**：85% (context_limit × 0.85)
+- **计算公式**：`max_tokens = context_limit × 1000 × 0.85`
+- **示例**：对于 128K 上下文限制
+  - 保留空间：`128 × 1000 × 0.85 = 108,800` tokens 用于输入
+  - 预留空间：`128,000 - 108,800 = 19,200` tokens 用于输出
+
+**适用场景**：
+1. **简短输出任务**
+   - 是/否判断
+   - 简短回答确认
+   - 状态查询类请求
+
+2. **分析类任务**
+   - 日志分析（输出分析结果，但不需要很长）
+   - 数据解读（输出简洁的结论）
+   - 配置检查（返回 OK 或简短说明）
+
+3. **输出比较确定的场景**
+   - 明确知道输出会很短
+   - 只需要简单确认或状态
+   - 不需要长篇解释的任务
+
+**优缺点**：
+- ✅ **优点**：最大化输入上下文，保留更多历史信息
+- ❌ **缺点**：输出容易被截断，不适用于长输出任务
+
+**日志示例**：
+```
+INFO: Context prepared: 50 msgs, ~105200 tokens / 128K limit (82.2%), strategy=aggressive
+INFO: Available for output: ~19200 tokens
+```
+
+---
+
+## 策略对比总结
+
+| 特性 | Conservative | Balanced | Aggressive |
+|------|-------------|----------|------------|
+| **输入比例** | 60% | 75% | 85% |
+| **输出预留** | 40% | 25% | 15% |
+| **上下文数量** | 最少 | 中等 | 最多 |
+| **输出空间** | 最大 | 中等 | 最小 |
+| **适用性** | 长输出 | 一般使用 | 短输出 |
+| **风险** | 上下文不足 | 平衡 | 输出截断 |
+
+**选择建议流程**：
+```
+1. 任务类型是什么？
+   ├─ 代码生成/长文档 → Conservative
+   ├─ 日常对话/一般任务 → Balanced (推荐)
+   └─ 简短确认/分析 → Aggressive
+
+2. 输出长度预估？
+   ├─ 不确定/可能很长 → Conservative
+   ├─ 中等长度 → Balanced
+   └─ 很短/简洁 → Aggressive
+
+3. 上下文重要性？
+   ├─ 早期历史不太重要 → Aggressive (更多上下文)
+   ├─ 需要平衡 → Balanced
+   └─ 重点是输出完整性 → Conservative (更多输出空间)
+```
+
+## 工作流程
+
+```
+用户发送消息
+     ↓
+获取消息历史 state["messages"]
+     ↓
+构建系统提示 + 拓扑信息
+     ↓
+调用 prepare_context_messages()
+     ↓
+  ├─ 估算 token 数量
+  ├─ 获取模型上下文限制
+  ├─ 判断是否需要裁剪
+  └─ 如果需要 → 调用 trim_messages()
+     ↓
+调用 LLM（带裁剪后的消息）
+     ↓
+返回响应
+```
+
+## 日志输出示例
+
+### 正常情况
+```
+INFO: Using database config context limit: 128000 tokens for model 'gpt-4o'
+INFO: Context prepared: 15 msgs, ~8432 tokens / 128000 limit (6.6%), strategy=balanced
+INFO: LLM call completed: tool_calls=2
+```
+
+### 发生裁剪时
+```
+INFO: Using database config context limit: 128000 tokens for model 'gpt-4o'
+INFO: Trimming messages: 18500 → 9600 tokens (model: gpt-4o)
+INFO: Trimmed 50 → 25 messages
+INFO: Context prepared: 27 msgs, ~9432 tokens / 128000 limit (7.4%), strategy=balanced
+```
+
+### 配置错误时
+```
+ERROR: context_limit is required but not provided for model 'gpt-4o'.
+       Please configure context_limit in your LLM model configuration.
+       Refer to the model provider's documentation for the current context window size.
+```
+
+## 错误处理
+
+### Token 计数失败
+```python
+try:
+    tokens = count_tokens_approximately(messages)
+except Exception as e:
+    logger.warning("Failed to count tokens: %s", e)
+    # 降级到简单的消息数量限制
+    return messages[-50:]
+```
+
+### 裁剪失败
+```python
+try:
+    trimmed = trim_messages(...)
+except Exception as e:
+    logger.error("Failed to trim: %s", e)
+    # 回退到简单的切片操作
+    return system_msgs + other_msgs[-N:]
+```
+
+## 参考资源
+
+- [LangChain Messages Utils](https://python.langchain.com/docs/messages/)
+- [LangGraph Memory Management](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#memory)
+- [OpenAI Models Context Limits](https://platform.openai.com/docs/models)
+- [Anthropic Models Context Limits](https://docs.anthropic.com/claude/docs/models-overview)
+
+## 相关源文件
+
+- `gns3server/agent/gns3_copilot/agent/context_manager.py` - 上下文管理核心逻辑
+- `gns3server/agent/gns3_copilot/agent/gns3_copilot.py` - LLM 调用节点
+- `gns3server/agent/gns3_copilot/agent_service.py` - Agent 服务接口
+
+## 总结
+
+通过使用 LangChain/LangGraph 的内置功能，我们实现了：
+1. ✅ 智能裁剪消息历史，避免超限
+2. ✅ **必须手动配置 context_limit**，确保使用正确的上下文窗口大小
+3. ✅ 可配置的裁剪策略（保守/平衡/激进）
+4. ✅ 始终保留系统消息和拓扑信息
+5. ✅ 详细的日志输出，便于调试
+6. ✅ 优雅的错误处理和明确的错误提示
+7. ✅ 提供参考工具，帮助查找常见模型的上下文限制
+
+### 关键优势
+
+- **准确性**：用户从官方文档获取最新的上下文限制，避免使用过时数据
+- **灵活性**：每个配置独立设置，支持不同用户使用不同限制
+- **明确性**：缺少配置时立即报错，避免静默失败
+- **可维护性**：无需维护内置默认值，减少代码维护负担
+- **可观测性**：详细日志显示上下文使用情况和裁剪决策
+
+这个实现确保了即使在进行长对话时，系统也不会因为上下文溢出而失败。
diff --git a/docs/gns3-copilot/llm-model-configs-api.md b/docs/gns3-copilot/llm-model-configs-api.md
index 5a867e4b2..a94b513f4 100644
--- a/docs/gns3-copilot/llm-model-configs-api.md
+++ b/docs/gns3-copilot/llm-model-configs-api.md
@@ -115,6 +115,7 @@ The `model_type` field accepts the following values:
 | `base_url` | string | API base URL |
 | `model` | string | Model name |
 | `temperature` | float | Temperature (0.0-2.0, default: 0.7) |
+| `context_limit` | integer | **Model context window limit in K tokens** (e.g., 128 = 128K = 128,000 tokens) |
 
 **Optional Fields:**
 
@@ -122,8 +123,17 @@ The `model_type` field accepts the following values:
 |-------|------|-------------|
 | `api_key` | string | API key (auto-encrypted) |
 | `max_tokens` | integer | Max tokens for generation |
+| `context_strategy` | string | Context trimming strategy: "conservative" (60%), "balanced" (75%), "aggressive" (85%). Default: "balanced" |
 | `is_default` | boolean | Set as default (default: false) |
 
+**Important Notes:**
+
+- **`context_limit` is required**: You must specify the model's context window limit. Refer to the model provider's official documentation for the current value.
+- **Unit is K tokens**: The value is in thousands of tokens (1 K = 1,000 tokens). For example:
+  - GPT-4o: 128,000 tokens → configure as `"context_limit": 128`
+  - Claude 3.5 Sonnet: 200,000 tokens → configure as `"context_limit": 200`
+  - Gemini 1.5 Pro: 2,800,000 tokens → configure as `"context_limit": 2800`
+
 **Extra Fields:** Any custom fields are supported for future extensibility.
 
 ### LLMModelConfigUpdate
@@ -138,6 +148,8 @@ The `model_type` field accepts the following values:
 | `temperature` | float (optional) | Temperature |
 | `api_key` | string (optional) | API key |
 | `max_tokens` | integer (optional) | Max tokens |
+| `context_limit` | integer (optional) | Model context window limit in K tokens |
+| `context_strategy` | string (optional) | Context trimming strategy |
 | `is_default` | boolean (optional) | Default flag |
 | `expected_version` | integer (optional) | **Optimistic locking version** |
 
@@ -213,28 +225,36 @@ curl -X POST http://localhost:3080/v3/access/users/{user_id}/llm-model-configs \
   -H "Authorization: Bearer <token>" \
   -H "Content-Type: application/json" \
   -d '{
-    "name": "GPT-4",
+    "name": "GPT-4o",
     "model_type": "text",
     "provider": "openai",
     "base_url": "https://api.openai.com/v1",
-    "model": "gpt-4",
+    "model": "gpt-4o",
     "temperature": 0.7,
+    "context_limit": 128,
+    "context_strategy": "balanced",
     "api_key": "sk-xxx",
     "is_default": true
   }'
 ```
 
+**Important:**
+- `context_limit` is **required** and specified in K tokens (e.g., 128 = 128K = 128,000 tokens)
+- Refer to the model provider's official documentation for the current context window size
+
 **Response:**
 ```json
 {
   "config_id": "uuid-1",
-  "name": "GPT-4",
+  "name": "GPT-4o",
   "model_type": "text",
   "config": {
     "provider": "openai",
     "base_url": "https://api.openai.com/v1",
-    "model": "gpt-4",
+    "model": "gpt-4o",
     "temperature": 0.7,
+    "context_limit": 128,
+    "context_strategy": "balanced",
     "api_key": "sk-xxx"
   },
   "user_id": "uuid-user",
@@ -253,12 +273,14 @@ curl -X POST http://localhost:3080/v3/access/groups/{group_id}/llm-model-configs
   -H "Authorization: Bearer <token>" \
   -H "Content-Type: application/json" \
   -d '{
-    "name": "Claude-3",
+    "name": "Claude-3.5 Sonnet",
     "model_type": "text",
     "provider": "anthropic",
     "base_url": "https://api.anthropic.com",
-    "model": "claude-3-opus-20240229",
+    "model": "claude-3-5-sonnet-20241022",
     "temperature": 0.7,
+    "context_limit": 200,
+    "context_strategy": "balanced",
     "api_key": "sk-ant-xxx",
     "is_default": true
   }'
@@ -277,13 +299,15 @@ curl -X GET http://localhost:3080/v3/access/users/{user_id}/llm-model-configs \
   "configs": [
     {
       "config_id": "uuid-1",
-      "name": "GPT-4",
+      "name": "GPT-4o",
       "model_type": "text",
       "config": {
         "provider": "openai",
         "base_url": "https://api.openai.com/v1",
-        "model": "gpt-4",
+        "model": "gpt-4o",
         "temperature": 0.7,
+        "context_limit": 128,
+        "context_strategy": "balanced",
         "api_key": "sk-xxx"
       },
       "user_id": "uuid-user",
@@ -297,13 +321,15 @@ curl -X GET http://localhost:3080/v3/access/users/{user_id}/llm-model-configs \
     },
     {
       "config_id": "uuid-2",
-      "name": "Claude-3",
+      "name": "Claude-3.5 Sonnet",
       "model_type": "text",
       "config": {
         "provider": "anthropic",
         "base_url": "https://api.anthropic.com",
-        "model": "claude-3-opus-20240229",
+        "model": "claude-3-5-sonnet-20241022",
         "temperature": 0.7,
+        "context_limit": 200,
+        "context_strategy": "balanced",
         "api_key": null
       },
       "user_id": null,
@@ -318,11 +344,16 @@ curl -X GET http://localhost:3080/v3/access/users/{user_id}/llm-model-configs \
   ],
   "default_config": {
     "config_id": "uuid-1",
-    "name": "GPT-4",
+    "name": "GPT-4o",
     "model_type": "text",
     "config": {
       "provider": "openai",
-      ...
+      "base_url": "https://api.openai.com/v1",
+      "model": "gpt-4o",
+      "temperature": 0.7,
+      "context_limit": 128,
+      "context_strategy": "balanced",
+      "api_key": "sk-xxx"
     },
     "user_id": "uuid-user",
     "group_id": null,
@@ -340,6 +371,7 @@ curl -X GET http://localhost:3080/v3/access/users/{user_id}/llm-model-configs \
 - `source: "user"` indicates the config belongs to the user
 - `source: "group"` indicates the config is inherited from a group
 - Configuration fields are nested in the `config` object (same structure as group endpoints)
+- `context_limit` is in K tokens (128 = 128K = 128,000 tokens)
 
 ### 4. Get group configurations
 
@@ -417,7 +449,8 @@ curl -X PUT http://localhost:3080/v3/access/users/{user_id}/llm-model-configs/{c
   -H "Content-Type: application/json" \
   -d '{
     "temperature": 0.9,
-    "max_tokens": 4000
+    "max_tokens": 4000,
+    "context_strategy": "aggressive"
   }'
 ```
 
@@ -482,13 +515,15 @@ curl -X GET http://localhost:3080/v3/access/users/{user_id}/llm-model-configs/de
 ```json
 {
   "config_id": "uuid-1",
-  "name": "GPT-4",
+  "name": "GPT-4o",
   "model_type": "text",
   "config": {
     "provider": "openai",
     "base_url": "https://api.openai.com/v1",
-    "model": "gpt-4",
+    "model": "gpt-4o",
     "temperature": 0.7,
+    "context_limit": 128,
+    "context_strategy": "balanced",
     "api_key": "sk-xxx"
   },
   "user_id": "uuid-user",
@@ -723,3 +758,82 @@ The `llm_model_configs` table includes three reserved JSONB fields for future ex
 - Caching computed values
 
 **Note:** These fields are not exposed in the current API schemas and are reserved for internal use.
+
+---
+
+## Context Limit Configuration
+
+### What is `context_limit`?
+
+The `context_limit` field specifies the maximum context window size for an LLM model. This is a **required field** for all model configurations.
+
+### Why is it required?
+
+Model providers frequently update their models and change context window sizes:
+- OpenAI GPT-4o: 128K tokens (may change)
+- Anthropic Claude 3.5: 200K tokens (may change)
+- Google Gemini 1.5: 2.8M tokens (may change)
+
+Hardcoding these values in the system would quickly become outdated. Requiring users to configure this field ensures that the system always uses the correct, up-to-date values.
+
+### Unit: K tokens
+
+The `context_limit` value is specified in **K tokens** (thousands of tokens) to make it easier to read and write:
+
+| Official Documentation | API Configuration |
+|---------------------|-------------------|
+| 128,000 tokens | `"context_limit": 128` |
+| 200,000 tokens | `"context_limit": 200` |
+| 2,800,000 tokens | `"context_limit": 2800` |
+
+### How to find the correct value
+
+1. **Check the official documentation** for your model:
+   - OpenAI: https://platform.openai.com/docs/models
+   - Anthropic: https://docs.anthropic.com/claude/docs/models-overview
+   - Google: https://ai.google.dev/gemini-api/docs/models
+   - DeepSeek: https://platform.deepseek.com/api-docs/
+
+2. **Convert from tokens to K**:
+   ```
+   context_limit = official_value_in_tokens / 1000
+
+   Example:
+   GPT-4o: 128,000 tokens / 1000 = 128
+   ```
+
+3. **Use the reference tool**:
+   ```bash
+   python scripts/show_model_context_limits.py
+   ```
+
+### Example: Common Models
+
+| Model | Official Value | Configuration |
+|-------|---------------|--------------|
+| GPT-4o | 128,000 | `"context_limit": 128` |
+| GPT-3.5 Turbo | 16,385 | `"context_limit": 17` |
+| Claude 3.5 Sonnet | 200,000 | `"context_limit": 200` |
+| Gemini 1.5 Pro | 2,800,000 | `"context_limit": 2800` |
+| DeepSeek Chat | 128,000 | `"context_limit": 128` |
+
+### Context Strategy
+
+The optional `context_strategy` field controls how aggressively the system uses the available context window:
+
+| Strategy | Usage | Best For |
+|----------|-------|----------|
+| `conservative` | 60% of limit | Long outputs, complex tasks, uncertain output size |
+| `balanced` (default) | 75% of limit | Most conversations, general use |
+| `aggressive` | 85% of limit | Short outputs, analysis tasks, predictable output size |
+
+### Error Handling
+
+If `context_limit` is missing or invalid, the API will return:
+
+```json
+HTTP 400 Bad Request
+{
+  "detail": "context_limit is required (unit: K tokens, e.g., 128 = 128K = 128,000 tokens). Please check your model provider's documentation for the current context window size and specify it in the configuration."
+}
+```
diff --git a/gns3server/agent/gns3_copilot/agent/context_manager.py b/gns3server/agent/gns3_copilot/agent/context_manager.py
new file mode 100644
index 000000000..cd9f6f10a
--- /dev/null
+++ b/gns3server/agent/gns3_copilot/agent/context_manager.py
@@ -0,0 +1,364 @@
+# SPDX-License-Identifier: AGPL-3.0-or-later
+#
+# Context Window Manager for GNS3-Copilot
+#
+# This module manages context window limits for different LLM models,
+# implementing intelligent message trimming and token counting strategies.
+
+"""
+Context Window Manager for GNS3-Copilot
+
+This module provides context window management for different LLM models,
+including:
+- Model-specific context window limits
+- Token counting for messages
+- Message trimming strategies
+- System message preservation
+"""
+
+import logging
+from typing import Any, Literal
+
+from langchain_core.messages import (
+    AIMessage,
+    HumanMessage,
+    SystemMessage,
+    ToolMessage,
+)
+from langchain_core.messages.utils import trim_messages
+
+logger = logging.getLogger(__name__)
+
+
+# NOTE: Built-in model context limits have been removed.
+# Model providers frequently update context limits, and maintaining this list is not sustainable.
+# Users MUST configure context_limit explicitly in their LLM model configurations.
+#
+# For reference, common model context limits as of 2025:
+# - OpenAI GPT-4o: 128K tokens
+# - OpenAI GPT-4 Turbo: 128K tokens
+# - OpenAI GPT-3.5 Turbo: 16K tokens
+# - Anthropic Claude 3.5 Sonnet: 200K tokens
+# - Google Gemini 1.5 Pro: 2.8M tokens
+# - DeepSeek Chat: 128K tokens
+#
+# Always verify current limits from official provider documentation.
+
+
+def get_model_context_limit(
+    model_name: str,
+    llm_config: dict[str, Any] | None = None
+) -> int:
+    """
+    Get the context window limit for a given model.
+
+    IMPORTANT: context_limit MUST be provided in llm_config.
+    Model providers frequently update context limits, so built-in defaults are NOT used.
+    Users must configure this value explicitly.
+
+    NOTE: context_limit unit is K tokens (1 K = 1000 tokens).
+    Example: 128 means 128K = 128,000 tokens.
+
+    Args:
+        model_name: Name of the model (e.g., "gpt-4o", "deepseek-chat")
+        llm_config: LLM config dict from database (must contain context_limit in K tokens)
+
+    Returns:
+        int: Maximum context window size in tokens (actual number, not K)
+
+    Raises:
+        ValueError: If context_limit is not provided or invalid
+    """
+    # Check database config for context_limit
+    if llm_config and "context_limit" in llm_config:
+        db_limit_k = llm_config["context_limit"]
+        if isinstance(db_limit_k, int) and db_limit_k > 0:
+            # Convert K tokens to actual tokens
+            actual_tokens = db_limit_k * 1000
+            logger.debug(
+                "Using database config context limit: %dK tokens (%d tokens) for model '%s'",
+                db_limit_k, actual_tokens, model_name
+            )
+            return actual_tokens
+        else:
+            raise ValueError(
+                f"Invalid context_limit in database config: {db_limit_k} "
+                f"(type={type(db_limit_k).__name__}, expected positive integer in K tokens)"
+            )
+
+    # No context_limit provided - this is a configuration error
+    raise ValueError(
+        f"context_limit is required but not provided for model '{model_name}'. "
+        f"Please configure context_limit in your LLM model configuration (unit: K tokens). "
+        f"Example: 128 means 128K = 128,000 tokens. "
+        f"Refer to the model provider's documentation for the current context window size."
+    )
+
+
+def calculate_max_tokens(
+    model_limit: int,
+    strategy: Literal["conservative", "balanced", "aggressive"] = "balanced"
+) -> int:
+    """
+    Calculate the maximum tokens to use, reserving space for output.
+
+    Args:
+        model_limit: Model's context window limit
+        strategy: How aggressively to use the context window
+            - "conservative": Use 60% of limit (safer, more reserved for output)
+            - "balanced": Use 75% of limit (default)
+            - "aggressive": Use 85% of limit (maximize input, minimal output reserve)
+
+    Returns:
+        int: Maximum tokens for input messages
+    """
+    ratios = {
+        "conservative": 0.60,
+        "balanced": 0.75,
+        "aggressive": 0.85,
+    }
+
+    ratio = ratios.get(strategy, 0.75)
+    max_tokens = int(model_limit * ratio)
+
+    logger.debug(
+        "Context limit: model=%d, strategy=%s, usable=%d tokens",
+        model_limit, strategy, max_tokens
+    )
+
+    return max_tokens
+
+
+def trim_messages_for_context(
+    messages: list[Any],
+    model_name: str,
+    llm_config: dict[str, Any] | None = None,
+    strategy: Literal["conservative", "balanced", "aggressive"] = "balanced",
+    preserve_system: bool = True,
+) -> list[Any]:
+    """
+    Trim messages to fit within model's context window.
+
+    This function uses LangChain's trim_messages utility to intelligently
+    reduce message history while preserving conversation flow.
+
+    Args:
+        messages: List of LangChain messages (HumanMessage, AIMessage, etc.)
+        model_name: Name of the LLM model being used
+        llm_config: Optional LLM config dict from database (may contain context_limit)
+        strategy: How aggressively to use the context window
+        preserve_system: Whether to always preserve system messages
+
+    Returns:
+        list: Trimmed list of messages that fit within context limit
+
+    Examples:
+        >>> messages = [HumanMessage("Hello"), AIMessage("Hi there!")]
+        >>> trimmed = trim_messages_for_context(messages, "gpt-4o")
+        >>> len(trimmed) <= len(messages)
+        True
+    """
+    if not messages:
+        return messages
+
+    # Get model's context limit (from database or built-in defaults)
+    model_limit = get_model_context_limit(model_name, llm_config)
+
+    # Calculate usable tokens (reserve space for output)
+    max_tokens = calculate_max_tokens(model_limit, strategy)
+
+    # Check if trimming is needed
+    try:
+        from langchain_core.messages.utils import count_tokens_approximately
+
+        # Estimate current token count
+        current_tokens = count_tokens_approximately(messages)
+
+        if current_tokens <= max_tokens:
+            logger.debug(
+                "Messages fit in context: %d / %d tokens",
+                current_tokens, max_tokens
+            )
+            return messages
+
+        logger.info(
+            "Trimming messages: %d → %d tokens (model: %s)",
+            current_tokens, max_tokens, model_name
+        )
+
+    except Exception as e:
+        logger.warning("Failed to count tokens: %s, proceeding with trim", e)
+
+    # Trim messages using LangChain's utility
+    try:
+        trimmed = trim_messages(
+            messages,
+            strategy="last",  # Keep most recent messages
+            max_tokens=max_tokens,
+            preserve_system=preserve_system,
+            start_on="human",  # Ensure we start with a human message
+            end_on=("human", "tool", "ai"),  # End on human/tool/ai messages
+        )
+
+        logger.info(
+            "Trimmed %d → %d messages",
+            len(messages), len(trimmed)
+        )
+
+        return trimmed
+
+    except Exception as e:
+        logger.error("Failed to trim messages: %s", e, exc_info=True)
+
+        # Fallback: simple slicing (keep last N messages)
+        # Estimate average tokens per message (~100 tokens)
+        fallback_msg_count = max(1, max_tokens // 100)
+
+        logger.warning(
+            "Using fallback trimming: keeping last %d messages",
+            fallback_msg_count
+        )
+
+        # Always preserve system messages
+        system_msgs = [m for m in messages if isinstance(m, SystemMessage)]
+        other_msgs = [m for m in messages if not isinstance(m, SystemMessage)]
+
+        return system_msgs + other_msgs[-fallback_msg_count:]
+
+
+def get_token_usage_summary(
+    messages: list[Any],
+    model_name: str,
+    llm_config: dict[str, Any] | None = None
+) -> dict[str, Any]:
+    """
+    Get a summary of token usage for the given messages.
+
+    Args:
+        messages: List of LangChain messages
+        model_name: Name of the LLM model
+        llm_config: Optional LLM config dict from database (may contain context_limit in K tokens)
+
+    Returns:
+        dict: Token usage summary including:
+            - estimated_tokens: Estimated total tokens
+            - model_limit_k: Model's context window limit in K tokens
+            - model_limit_tokens: Model's context window limit in actual tokens
+            - usage_percentage: Percentage of context used
+            - message_count: Number of messages
+            - needs_trimming: Whether messages exceed 80% of limit
+    """
+    try:
+        from langchain_core.messages.utils import count_tokens_approximately
+
+        estimated_tokens = count_tokens_approximately(messages)
+    except Exception as e:
+        logger.warning("Failed to count tokens: %s", e)
+        estimated_tokens = 0
+
+    model_limit_tokens = get_model_context_limit(model_name, llm_config)
+    model_limit_k = model_limit_tokens // 1000
+    usage_percentage = (estimated_tokens / model_limit_tokens * 100) if model_limit_tokens > 0 else 0
+
+    return {
+        "estimated_tokens": estimated_tokens,
+        "model_limit_k": model_limit_k,
+        "model_limit_tokens": model_limit_tokens,
+        "usage_percentage": round(usage_percentage, 2),
+        "message_count": len(messages),
+        "needs_trimming": usage_percentage > 80,
+    }
+
+
+# Convenience function for GNS3-Copilot integration
+def prepare_context_messages(
+    state_messages: list[Any],
+    system_prompt: str,
+    topology_context: str | None,
+    model_name: str,
+    llm_config: dict[str, Any] | None = None,
+) -> list[Any]:
+    """
+    Prepare full context messages for LLM call with automatic trimming.
+
+    This is the main entry point for GNS3-Copilot to prepare messages
+    before calling the LLM.
+
+    Args:
+        state_messages: Message history from conversation state
+        system_prompt: System prompt text
+        topology_context: Optional topology information string
+        model_name: Name of the LLM model
+        llm_config: Optional LLM config dict from database (may contain context_limit and context_strategy)
+
+    Returns:
+        list: Prepared messages ready for LLM invocation
+
+    Examples:
+        >>> messages = prepare_context_messages(
+        ...     state_messages=[HumanMessage("Help me")],
+        ...     system_prompt="You are a helpful assistant",
+        ...     topology_context=None,
+        ...     model_name="gpt-4o"
+        ... )
+        >>> len(messages)
+        2  # System message + Human message
+    """
+    # Get trimming strategy from config (default: "balanced")
+    trim_strategy = "balanced"
+    if llm_config and "context_strategy" in llm_config:
+        strategy = llm_config["context_strategy"]
+        if strategy in ["conservative", "balanced", "aggressive"]:
+            trim_strategy = strategy
+            logger.debug("Using context_strategy from config: %s", trim_strategy)
+        else:
+            logger.warning("Invalid context_strategy '%s', using 'balanced'", strategy)
+
+    # Build base context (system + topology)
+    context_messages = [SystemMessage(content=system_prompt)]
+
+    if topology_context:
+        context_messages.append(
+            SystemMessage(content=f"Current Topology:\n{topology_context}")
+        )
+
+    # Combine with conversation history
+    full_messages = context_messages + state_messages
+
+    # Trim if needed
+    trimmed_messages = trim_messages_for_context(
+        full_messages,
+        model_name=model_name,
+        llm_config=llm_config,
+        strategy=trim_strategy,
+        preserve_system=True,  # Always keep system prompts
+    )
+
+    # Log summary
+    summary = get_token_usage_summary(trimmed_messages, model_name, llm_config)
+    logger.info(
+        "Context prepared: %d msgs, ~%d tokens / %dK limit (%.1f%%), strategy=%s",
+        summary["message_count"],
+        summary["estimated_tokens"],
+        summary["model_limit_k"],
+        summary["usage_percentage"],
+        trim_strategy
+    )
+
+    return trimmed_messages
+
+
+if __name__ == "__main__":
+    # Simple test
+    test_messages = [
+        HumanMessage(f"Message {i}") for i in range(100)
+    ]
+
+    result = trim_messages_for_context(
+        test_messages,
+        model_name="gpt-4o",
+        strategy="balanced"
+    )
+
+    print(f"Original: {len(test_messages)} messages")
+    print(f"Trimmed: {len(result)} messages")
diff --git a/gns3server/agent/gns3_copilot/agent/gns3_copilot.py b/gns3server/agent/gns3_copilot/agent/gns3_copilot.py
index 69fd58f04..1e821122a 100644
--- a/gns3server/agent/gns3_copilot/agent/gns3_copilot.py
+++ b/gns3server/agent/gns3_copilot/agent/gns3_copilot.py
@@ -48,6 +48,9 @@ from gns3server.agent.gns3_copilot.agent.model_factory import (
     create_base_model_with_tools,
     create_title_model,
 )
+from gns3server.agent.gns3_copilot.agent.context_manager import (
+    prepare_context_messages,
+)
 from gns3server.agent.gns3_copilot.gns3_client import GNS3TopologyTool
 from gns3server.agent.gns3_copilot.prompts import TITLE_PROMPT, load_system_prompt
 import sys
@@ -164,8 +167,8 @@ def llm_call(state: dict, config: RunnableConfig | None = None):
     if config and config.get("configurable"):
         project_id = config["configurable"].get("project_id")
 
-    # Construct context messages
-    context_messages = []
+    # Retrieve topology information if available
+    topology_context = None
     topology_info = None
 
     if project_id:
@@ -180,12 +183,8 @@ def llm_call(state: dict, config: RunnableConfig | None = None):
                     "Successfully retrieved topology for project_id: %s, name: %s",
                     project_id, topology.get("name")
                 )
-
                 # Convert topology dict to string for LLM consumption
                 topology_context = str(topology)
-                context_messages.append(
-                    SystemMessage(content=f"Current Topology:\n{topology_context}")
-                )
             else:
                 logger.warning(
                     "Failed to retrieve topology for project_id %s: %s",
@@ -194,11 +193,18 @@ def llm_call(state: dict, config: RunnableConfig | None = None):
         except Exception as e:
             logger.warning("Error retrieving topology for project_id %s: %s", project_id, e)
 
-    # Merge message lists
-    full_messages = (
-        [SystemMessage(content=current_prompt)] + context_messages + state["messages"]
+    # Prepare context messages with automatic trimming based on model's context window
+    # This ensures we don't exceed the model's context limit
+    # llm_config may contain:
+    # - context_limit: Override built-in context window limit
+    # - context_strategy: Trimming strategy (conservative/balanced/aggressive)
+    full_messages = prepare_context_messages(
+        state_messages=state["messages"],
+        system_prompt=current_prompt,
+        topology_context=topology_context,
+        model_name=llm_config.get("model", "default"),
+        llm_config=llm_config,
     )
-    # print(full_messages)
 
     # Create fresh model with tools for each LLM call
     logger.debug("Creating model with tools: provider=%s, model=%s",
diff --git a/gns3server/api/routes/controller/llm_model_configs.py b/gns3server/api/routes/controller/llm_model_configs.py
index 0e4dbcbd4..da36de67b 100644
--- a/gns3server/api/routes/controller/llm_model_configs.py
+++ b/gns3server/api/routes/controller/llm_model_configs.py
@@ -182,6 +182,9 @@ async def create_user_llm_model_config(
     Create a new LLM model configuration for a user.
 
     Required privilege: User.Modify
+
+    IMPORTANT: context_limit is REQUIRED. Unit is K tokens (e.g., 128 = 128K = 128,000 tokens).
+    Please check your model provider's documentation for the current context window size.
     """
 
     # Verify user exists
@@ -189,6 +192,14 @@ async def create_user_llm_model_config(
     if not user:
         raise ControllerNotFoundError(f"User '{user_id}' not found")
 
+    # Validate context_limit is provided
+    if not hasattr(config_create, 'context_limit') or config_create.context_limit is None:
+        raise ControllerBadRequestError(
+            "context_limit is required (unit: K tokens, e.g., 128 = 128K = 128,000 tokens). "
+            "Please check your model provider's documentation for the current context window size "
+            "and specify it in the configuration."
+        )
+
     try:
         # Extract config fields (excluding table-level fields)
         config_fields = config_create.model_dump(exclude={"name", "model_type", "is_default"})
@@ -493,6 +504,9 @@ async def create_group_llm_model_config(
     Create a new LLM model configuration for a user group.
 
     Required privilege: Group.Modify
+
+    IMPORTANT: context_limit is REQUIRED. Unit is K tokens (e.g., 128 = 128K = 128,000 tokens).
+    Please check your model provider's documentation for the current context window size.
     """
 
     # Verify group exists
@@ -500,6 +514,14 @@ async def create_group_llm_model_config(
     if not group:
         raise ControllerNotFoundError(f"User group '{group_id}' not found")
 
+    # Validate context_limit is provided
+    if not hasattr(config_create, 'context_limit') or config_create.context_limit is None:
+        raise ControllerBadRequestError(
+            "context_limit is required (unit: K tokens, e.g., 128 = 128K = 128,000 tokens). "
+            "Please check your model provider's documentation for the current context window size "
+            "and specify it in the configuration."
+        )
+
     try:
         # Extract config fields (excluding table-level fields)
         config_fields = config_create.model_dump(exclude={"name", "model_type", "is_default"})
diff --git a/gns3server/schemas/controller/llm_model_configs.py b/gns3server/schemas/controller/llm_model_configs.py
index 9b70f3ec6..7e4961e8b 100644
--- a/gns3server/schemas/controller/llm_model_configs.py
+++ b/gns3server/schemas/controller/llm_model_configs.py
@@ -30,6 +30,12 @@ class LLMModelConfigData(BaseModel):
     """
     LLM model configuration data.
     Stored in the config JSONB column (provider, base_url, model, etc.).
+
+    IMPORTANT: context_limit is REQUIRED to ensure proper context window management.
+    Model providers frequently update context limits, so users must configure this value.
+
+    NOTE: context_limit unit is K tokens (1 K = 1000 tokens).
+    Example: 128 means 128K tokens (128,000 tokens).
     """
 
     provider: str = Field(..., description="LLM provider (e.g., 'openai', 'anthropic', 'ollama')")
@@ -38,6 +44,10 @@ class LLMModelConfigData(BaseModel):
     temperature: float = Field(default=0.7, ge=0.0, le=2.0, description="Temperature parameter")
     api_key: Optional[str] = Field(None, description="API key (will be encrypted)")
     max_tokens: Optional[int] = Field(None, gt=0, description="Max tokens for generation")
+    context_limit: int = Field(..., gt=0, description="Model context window limit in K tokens (REQUIRED, e.g., 128 = 128K = 128,000 tokens)")
+    context_strategy: Literal["conservative", "balanced", "aggressive"] = Field(
+        "balanced", description="Context trimming strategy: conservative (60%), balanced (75%), aggressive (85%)"
+    )
 
     # Allow extra fields for extensibility
     model_config = ConfigDict(extra="allow")
@@ -57,6 +67,10 @@ class LLMModelConfigCreate(BaseModel):
     temperature: float = Field(default=0.7, ge=0.0, le=2.0)
     api_key: Optional[str] = None
     max_tokens: Optional[int] = Field(None, gt=0)
+    context_limit: int = Field(..., gt=0, description="Model context window limit in K tokens (REQUIRED, e.g., 128 = 128K tokens)")
+    context_strategy: Literal["conservative", "balanced", "aggressive"] = Field(
+        "balanced", description="Context trimming strategy"
+    )
 
     # Allow extra config fields
     model_config = ConfigDict(extra="allow")
@@ -78,6 +92,10 @@ class LLMModelConfigUpdate(BaseModel):
     temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
     api_key: Optional[str] = None
     max_tokens: Optional[int] = Field(None, gt=0)
+    context_limit: Optional[int] = Field(None, gt=0, description="Model context window limit in K tokens (e.g., 128 = 128K tokens)")
+    context_strategy: Optional[Literal["conservative", "balanced", "aggressive"]] = Field(
+        None, description="Context trimming strategy"
+    )
 
     # Allow extra config fields
     model_config = ConfigDict(extra="allow")
diff --git a/scripts/show_model_context_limits.py b/scripts/show_model_context_limits.py
new file mode 100644
index 000000000..f07475e4a
--- /dev/null
+++ b/scripts/show_model_context_limits.py
@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+"""
+Reference tool for LLM model context limits.
+
+This script provides reference context limit values for common LLM models.
+It helps users find the correct context_limit value when creating LLM model configurations.
+
+IMPORTANT:
+- context_limit unit is K tokens (1 K = 1000 tokens)
+- This tool only displays reference values. You MUST manually configure
+  context_limit when creating or updating LLM model configurations via API.
+
+Usage:
+    python scripts/show_model_context_limits.py
+
+For official documentation, always check:
+- OpenAI: https://platform.openai.com/docs/models
+- Anthropic: https://docs.anthropic.com/claude/docs/models-overview
+- Google: https://ai.google.dev/gemini-api/docs/models
+- DeepSeek: https://platform.deepseek.com/api-docs/
+"""
+
+import sys
+from pathlib import Path
+
+# Add parent directory to path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+
+# Reference context limits (as of 2025)
+# Displayed in K tokens for easier configuration
+# Users should verify from official provider documentation
+MODEL_CONTEXT_LIMITS_K = {
+    # OpenAI Models
+    "gpt-4o": 128,
+    "gpt-4o-mini": 128,
+    "gpt-4-turbo": 128,
+    "gpt-4": 8,
+    "gpt-4-32k": 33,
+    "gpt-3.5-turbo": 17,
+    "gpt-3.5-turbo-16k": 17,
+
+    # Anthropic Models
+    "claude-3-5-sonnet-20241022": 200,
+    "claude-3-5-sonnet-20240620": 200,
+    "claude-3-opus-20240229": 200,
+    "claude-3-sonnet-20240229": 200,
+    "claude-3-haiku-20240307": 200,
+
+    # Google Models
+    "gemini-2.0-flash-exp": 1000,
+    "gemini-1.5-pro": 2800,
+    "gemini-1.5-flash": 2800,
+    "gemini-pro": 92,
+
+    # DeepSeek Models
+    "deepseek-chat": 128,
+    "deepseek-coder": 128,
+
+    # xAI Models
+    "grok-beta": 128,
+}
+
+
+def find_context_limit_for_model(model_name: str) -> int | None:
+    """Find the context limit for a given model name (in K tokens)."""
+    model_lower = model_name.lower().strip()
+
+    # Try exact match
+    if model_lower in MODEL_CONTEXT_LIMITS_K:
+        return MODEL_CONTEXT_LIMITS_K[model_lower]
+
+    # Try prefix match
+    for key, limit in MODEL_CONTEXT_LIMITS_K.items():
+        if model_lower.startswith(key.lower()):
+            return limit
+
+    return None
+
+
+def main():
+    print("=" * 70)
+    print("LLM Model Context Limits Reference Tool")
+    print("=" * 70)
+    print()
+    print("This tool displays reference context limit values for common LLM models.")
+    print("Please verify from official provider documentation before configuring.")
+    print()
+    print("IMPORTANT: context_limit unit is K tokens (1 K = 1,000 tokens)")
+    print()
+    print("Official Documentation:")
+    print("  - OpenAI: https://platform.openai.com/docs/models")
+    print("  - Anthropic: https://docs.anthropic.com/claude/docs/models-overview")
+    print("  - Google: https://ai.google.dev/gemini-api/docs/models")
+    print("  - DeepSeek: https://platform.deepseek.com/api-docs/")
+    print()
+
+    # Display all reference values
+    print("=" * 70)
+    print("Reference Context Limits (in K tokens)")
+    print("=" * 70)
+    print()
+
+    # Group by provider
+    providers = {
+        "OpenAI": ["gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4", "gpt-4-32k", "gpt-3.5-turbo", "gpt-3.5-turbo-16k"],
+        "Anthropic": ["claude-3-5-sonnet-20241022", "claude-3-5-sonnet-20240620", "claude-3-opus-20240229", "claude-3-sonnet-20240229", "claude-3-haiku-20240307"],
+        "Google": ["gemini-2.0-flash-exp", "gemini-1.5-pro", "gemini-1.5-flash", "gemini-pro"],
+        "DeepSeek": ["deepseek-chat", "deepseek-coder"],
+        "xAI": ["grok-beta"],
+    }
+
+    for provider, models in providers.items():
+        print(f"\n{provider}:")
+        for model in models:
+            if model in MODEL_CONTEXT_LIMITS_K:
+                limit_k = MODEL_CONTEXT_LIMITS_K[model]
+                limit_actual = limit_k * 1000
+                print(f"  {model:40s} → {limit_k:4d}K (= {limit_actual:,} tokens)")
+
+    print()
+    print("=" * 70)
+    print()
+    print("Conversion Examples:")
+    print()
+    print("  Official Documentation: 128,000 tokens")
+    print("  ↓")
+    print("  API Configuration: \"context_limit\": 128")
+    print()
+    print("  Official Documentation: 200,000 tokens")
+    print("  ↓")
+    print("  API Configuration: \"context_limit\": 200")
+    print()
+    print("  Official Documentation: 2,800,000 tokens")
+    print("  ↓")
+    print("  API Configuration: \"context_limit\": 2800")
+    print()
+    print("=" * 70)
+    print()
+    print("Usage Example:")
+    print()
+    print("When creating a model configuration, specify context_limit in K:")
+    print()
+    print('  POST /v3/users/{user_id}/llm-model-configs')
+    print('  {')
+    print('    "name": "GPT-4o Configuration",')
+    print('    "provider": "openai",')
+    print('    "model": "gpt-4o",')
+    print('    "context_limit": 128,  // ← Required: 128K = 128,000 tokens')
+    print('    "context_strategy": "balanced"')
+    print('  }')
+    print()
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    main()